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Matlab编写的SPIHT 也叫层树分集压缩算法 本算法目前适合处理8*8的矩阵 <由于时间关系 本算法还不能对大的图像进行操作 但是主要的思想算法都在 只要稍做修改即可>希望对大家会有帮助 (Matlab prepared SPIHT also called tree diversity layer compression algorithm suitable for the processing algorithm currently 8* 8 the Matrix)

Fuzzy-PI Current Controlled D-STATCOM

matlab是一门简单易学的重要工具，一个简明的例程可以事半功倍。(matlab is an easy to learn an important tool for a concise routines can do more with less.)

实现了PID控制与自适应模糊PID控制的比较 亲测simulink仿真结果 (Adaptive fuzzy PID control)

一篇很好的将滑模控制与扩展卡尔曼控制结合的控制策略文章。(A very good text with SMO and EKF about PMSM )

Visual c++Matlab图像处理与实用案例精选:图像镜像(Visual c Matlab Image Processing and practical case Featured : Mirror Image)

Matlab中LVQ神经网络的分类——乳腺肿瘤诊断(LVQ neural network classification- breast cancer diagnosis)

MATLAB GUI设计学习手记（第二版）源程序 第3章 本书推荐的学习方法： 1）若您是一个MATLAB初学者，且有充裕的时间来学习GUI设计，您可以从本书的第1章开始循序渐进地学习。 2）若您是一位MATLAB初学者，且时间非常紧迫，建议您先学好第1章，然后依次学习第4章、第5章和第6章，其他章节请根据项目需要有选择性地学习。 3）若您已经掌握了MATLAB的基础语法知识，有一定的MATLAB编程基础，也请浏览一下第1章的内容，尤其是专题分析部分。然后先学习本书第4、5和6章，其他章节请根据项目需要有选择性地学习。 4）若您是一位MATLAB GUI方面的专家或老师，您可以将本书作为一本教学的参考书，本书内容全面、讲解详实、代码精炼，是不可多得的GUI宝典。 (MATLAB GUI design learning Hearts (second edition) source Chapter 3 the learning methods recommended in this book: 1) If you are a MATLAB beginners, and have plenty of time to learn GUI design, you can start from Chapter 1 of the book The step-by-step learning. 2) If you are a MATLAB beginners, and the time is very tight, it is recommended that you first learn to Chapter 1, followed by learning Chapter 4, Chapter 5 and Chapter 6, in other sections based on project needs selective learning . 3) If you have mastered the basic grammar of the MATLAB, the MATLAB Programming Fundamentals, please visit the Chapter 1, in particular thematic analysis section. First learning book chapters 4, 5 and 6, then the other chapters selective learning based on project needs. 4) If you are an expert or teacher of a MATLAB GUI, you can book as a teaching reference books, the book is comprehensive, explain the detailed, refined code, is a rare GUI canon.)

This program simulates the transmission performance of BPSK under Additive White Gausian Noise (AWGN) environment.

face recognition for person identification